﻿A new species of <i>Svistella</i> Gorochov, 1987 from Xizang, China (Orthoptera, Trigonidiidae, Trigonidiinae)

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Abstract

The genus Svistella Gorochov, 1987 includes 10 species from Asia, with nine documented in China. In this study, a new species, Svistella yayun He, sp. nov., is described from Xizang, China. Morphologically, it resembles S. rufonotata (Chopard, 1932) but can be distinguished by a smaller inner tympanum, dark-brown setae on the 5 ^th segment of the maxillary palp, and a rounded apex on the ectoparamere. To validate our morphological inferences and support the description of S. yayun sp. nov. as a new species, we performed a PCA based on bioacoustics parameters and molecular analysis. All Svistella species documented in China are distinguished by integrating their songs and DNA barcoding.

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MEGA11: Molecular Evolutionary Genetics Analysis Version 11

Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)

The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .

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ABGD, Automatic Barcode Gap Discovery for primary species delimitation.

N. Puillandre, A Lambert, S. Brouillet … (2012)

Within uncharacterized groups, DNA barcodes, short DNA sequences that are present in a wide range of species, can be used to assign organisms into species. We propose an automatic procedure that sorts the sequences into hypothetical species based on the barcode gap, which can be observed whenever the divergence among organisms belonging to the same species is smaller than divergence among organisms from different species. We use a range of prior intraspecific divergence to infer from the data a model-based one-sided confidence limit for intraspecific divergence. The method, called Automatic Barcode Gap Discovery (ABGD), then detects the barcode gap as the first significant gap beyond this limit and uses it to partition the data. Inference of the limit and gap detection are then recursively applied to previously obtained groups to get finer partitions until there is no further partitioning. Using six published data sets of metazoans, we show that ABGD is computationally efficient and performs well for standard prior maximum intraspecific divergences (a few per cent of divergence for the five data sets), except for one data set where less than three sequences per species were sampled. We further explore the theoretical limitations of ABGD through simulation of explicit speciation and population genetics scenarios. Our results emphasize in particular the sensitivity of the method to the presence of recent speciation events, via (unrealistically) high rates of speciation or large numbers of species. In conclusion, ABGD is fast, simple method to split a sequence alignment data set into candidate species that should be complemented with other evidence in an integrative taxonomic approach. © 2011 Blackwell Publishing Ltd.

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ASAP: assemble species by automatic partitioning

Nicolas Puillandre, Sophie Brouillet, Guillaume Achaz (2021)

Here, we describe Assemble Species by Automatic Partitioning (ASAP), a new method to build species partitions from single locus sequence alignments (i.e., barcode data sets). ASAP is efficient enough to split data sets as large 104 sequences into putative species in several minutes. Although grounded in evolutionary theory, ASAP is the implementation of a hierarchical clustering algorithm that only uses pairwise genetic distances, avoiding the computational burden of phylogenetic reconstruction. Importantly, ASAP proposes species partitions ranked by a new scoring system that uses no biological prior insight of intraspecific diversity. ASAP is a stand-alone program that can be used either through a graphical web-interface or that can be downloaded and compiled for local usage. We have assessed its power along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode data sets representing various degrees of challenge (from small and easy cases to large and complicated data sets). We also used Monte-Carlo simulations of a multispecies coalescent framework to assess the strengths and weaknesses of ASAP and the other programs. Through these analyses, we demonstrate that ASAP has the potential to become a major tool for taxonomists as it proposes rapidly in a full graphical exploratory interface relevant species hypothesis as a first step of the integrative taxonomy process.

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Author and article information

Contributors

Jing-Wen Hou:

ORCID: https://orcid.org/0009-0006-9395-8040

Role: Writing - original draftRole: Writing - review and editingRole: Data curationRole: Methodology

Yue Xu:

ORCID: https://orcid.org/0009-0005-7235-1613

Role: Writing - original draftRole: Writing - review and editingRole: Data curationRole: Methodology

Tian-Hao Hu:

ORCID: https://orcid.org/0000-0003-3210-961X

Role: Writing - original draftRole: Writing - review and editing

Zi-Heng Zhang:

ORCID: https://orcid.org/0009-0002-1944-2776

Role: Data curationRole: Methodology

Shi-Yang Wu:

ORCID: https://orcid.org/0000-0001-6812-1878

Role: Writing - review and editingRole: Methodology

Pu Gong:

ORCID: https://orcid.org/0000-0002-7050-6643

Role: Writing - review and editingRole: Supervision

Zhu-Qing He:

ORCID: https://orcid.org/0000-0003-4304-767X

Role: Writing - review and editingRole: Project administrationRole: Supervision

Journal

Journal ID (nlm-ta): Zookeys

Journal ID (iso-abbrev): Zookeys

Journal ID (publisher-id): 2

Journal ID (index): urn:lsid:arphahub.com:pub:45048D35-BB1D-5CE8-9668-537E44BD4C7E

Journal ID (aggregator): urn:lsid:zoobank.org:pub:91BD42D4-90F1-4B45-9350-EEF175B1727A

Title: ZooKeys

Publisher: Pensoft Publishers

ISSN (Print): 1313-2989

ISSN (Electronic): 1313-2970

Publication date Collection: 2024

Publication date (Electronic): 06 March 2024

Volume: 1193

Pages: 145-160

Affiliations

[1 ] School of Life Science, East China Normal University, Shanghai 200241, China East China Normal University Shanghai China

[2 ] Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA University of California Berkeley United States of America

[3 ] Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, 310051, China Zhejiang Provincial Center for Disease Control and Prevention Hangzhou China

Author notes

Corresponding authors: Pu Gong ( pgong@ 123456cdc.zj.cn) ; Zhu-Qing He ( zqhe@ 123456bio.ecnu.edu.cn)

Academic editor: Jun-Jie Gu

Author information

Jing-Wen Hou https://orcid.org/0009-0006-9395-8040

Yue Xu https://orcid.org/0009-0005-7235-1613

Tian-Hao Hu https://orcid.org/0000-0003-3210-961X

Zi-Heng Zhang https://orcid.org/0009-0002-1944-2776

Shi-Yang Wu https://orcid.org/0000-0001-6812-1878

Pu Gong https://orcid.org/0000-0002-7050-6643

Zhu-Qing He https://orcid.org/0000-0003-4304-767X

Article

Publisher ID: 117612

DOI: 10.3897/zookeys.1193.117612

PMC ID: 10938059

PubMed ID: 38487668

SO-VID: 98766e13-2d8a-4030-84c1-1a6b318a8246

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 20 December 2023

Date accepted : 01 February 2024

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